Abstract
The development and integration of Renewable Energy Sources (RES) will leadto a more sustainable energy future. However, several challenges arise for the
power grid design and operation, related to the increase of uncertainties in energy forecast, the increase in the variability and intermittency of renewable generation, and the decrease of system inertia. These factors affect the energy balance of the system and thus, the grid stability and reliability could worsen, increasing the amount of required frequency ancillary services (also known as reserve services). Meanwhile, the contribution of renewable energies is increasing in the energy mix, RES plants should improve their participation and operation through electricity markets in a more controllable and reliable way. Additionally, the current market design is being changed to allow inclusive participation of RES plants and new flexible participants in well-rewarded flexibility markets (such as reserve markets).
In this context, Energy Storage Systems (ESS) are considered one of the key
flexible technologies which can support RES operation (RES-oriented services)
and grid services at the same time, such as 1) RES capacity firming, 2) production
predictability improvement, and 3) provision of ancillary services. However, the ESS widespread deployment has been restricted by their high technology costs. Thus, this PhD thesis deals with the topic of the “Development of Optimal Energy Management and Sizing Strategies for Large-Scale Electrical Storage Systems supporting Renewable Energy Sources”, with the objective of developing a methodology with a global perspective, in which an advanced energy management strategy (EMS) addresses the RES+ESS asset management for the
long-term planning, and the optimal sizing and operation of electro-chemical ESS in the short term (at real-time), and ensures the proper framework to evaluate the cost-effectiveness of ESS integration on grid-connected applications.
Consequently, the main objectives of the proposed EMS are the following: i)
optimize energy and reserve market scheduling and minimize market penalties
and energy imbalances, regarding most recent forecast information, ii) re-schedule RES generation intra-daily to control forecast errors and manage ESSs, iii) provide power set points to the grid in real-time operation according to centralized or decentralized controls, iv) implement a closed-loop model predictive control, and v) evaluate and estimate properly the ESS lifetime through aging models. The proposed EMS is validated by means of two case studies: Firstly, stand-alone RES+ESS plants operate independently (considering a wind or solar plant with an energy storage system), and secondly, RES portfolio with distributed ESS is scheduled and operated according to different real-time supervisory controls.
| Date of Award | 2020 |
|---|---|
| Original language | English |
| Awarding Institution |
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